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Journal of Women's Health
 
J Womens Health (Larchmt). Apr 2009; 18(4): 443–450.
PMCID: PMC2684980
NIHMSID: NIHMS88073

The Joint Impact of Smoking and Exercise Capacity on Clinical Outcomes among Women with Suspected Myocardial Ischemia: The WISE Study

Sarah E. Linke, M.S.,corresponding author1 Thomas Rutledge, Ph.D.,2,,3 B. Delia Johnson, Ph.D.,4 Marian B. Olson, M.S.,4 Vera Bittner, M.D., MSPH,5 Carol E. Cornell, Ph.D.,6 Leslee J. Shaw, Ph.D.,6 Wafia Eteiba, M.D.,4 Susmita Parashar, M.D., M.P.H., M.S.,7 David S. Sheps, M.D., MSPH,8,,9 Diane A. Vido, M.S.,10 Suresh Mulukutla, M.D.,4 and C. Noel Bairey Merz, M.D.11

Abstract

Background

Although extensive research has been conducted on both smoking and low exercise capacity alone, few studies have examined the joint impact or interaction of these two risk factors. We examined the joint and interactive effects of smoking and self-reported exercise capacity on subsequent clinical events (heart failure, myocardial infarction [MI], stroke, and cardiovascular-related mortality) among women with suspected myocardial ischemia.

Methods

At baseline (1996–1999), 789 women completed angiographic testing of coronary artery disease (CAD) severity and provided self-report information about their smoking history and exercise capacity as well as demographic and other risk factor data. Incidence of clinical events among the women was tracked for a median of 5.9 years; this analysis was conducted in 2008.

Results

In an adjusted survival analysis, women with a positive smoking history and self-reported low exercise capacity had the greatest risk of experiencing a clinical event (HR = 7.7, 95% CI 2.3, 25.5), followed by women with a positive smoking history and self-reported high exercise capacity (HR = 6.9, 95% CI 2.0, 24.6) and those with a negative smoking history and self-reported low exercise capacity (HR = 4.9, 95% CI 1.5, 15.8), relative to women with a negative smoking history and self-reported high exercise capacity. Additional analyses revealed a significant interaction between smoking history and exercise capacity, such that (1) women with a positive smoking history did not experience an additional significantly greater risk due to low exercise capacity, unlike those with a negative smoking history, and (2) all women experienced a significantly greater risk due to a positive smoking history regardless of their exercise capacity.

Conclusions

Among women with suspected myocardial ischemia, the combined protective health effects of self-reported high exercise capacity and a negative smoking history remained significant after controlling for preexisting CAD severity and other established risk factors. These findings highlight the importance of studying behavioral risk factors in combination.

Introduction

Smoking and physical inactivity are among the leading causes of preventable death1—accounting for approximately one third of annual deaths2—as well as of serious diseases, such as cardiovascular disease (CVD) and stroke.3,4 Chronic inactivity contributes to reduced exercise capacity, a term often used interchangeably with functional capacity,5 another powerful predictor of prognosis.6,7 Although extensive research has been conducted on smoking8 and exercise capacity9 alone, as well as the two along with other cardiovascular risk factors,1012 few (if any) studies have examined the sole joint impact or interaction of these two risk factors. This gap in the literature is important to fill, particularly because these two risk factors frequently co-occur and are the two most significant modifiable risk factors for cardiovascular-related diseases3,4 and overall mortality.1

Population prevalence estimates among women indicate that approximately 18.5% are current smokers, ranging from 8.1% of ≥65-year-olds to 21.5% of 18–24-year-olds.13 Furthermore, 11.7% are never active, ranging from 6.4% of 18–24-year-olds to 26.1% of ≥65-year-olds, and only 28.5% engage in regular leisure time physical activity.14 Furthermore, approximately 16.2% of women have low fitness levels,15 a figure that is much greater among women with symptoms of cardiovascular-related problems.16,17

A distinction between physical activity and purposeful exercise has been made: physical activity is defined as bodily movement produced by skeletal muscles that requires energy expenditure and produces progressive health benefits, whereas exercise is described as planned physical activity with bodily movements that are structured and repetitively performed for the purpose of improving or maintaining physical fitness.18 Although any form of physical activity promotes general health,19 research supports the additional benefit of purposeful exercise20,21 and more vigorous exercise,2224 especially for their positive effect on cardiovascular health,25,26 specifically in early postmenopausal women.27

Although objective measures, such as treadmill tests, are optimal for determining exercise capacity, many self-report measures of physical capability have demonstrated validity and reliability.28,29 One specific questionnaire, the Duke Activity Status Index (DASI),30 a self-assessment of functional capability, was highly correlated with actual physical capacity measured in metabolic equivalents, as estimated during symptom-limited exercise treadmill testing among women with suspected myocardial ischemia.31 Additionally, a reduced DASI, composed of selected questions from the original version, was also correlated with Canadian Cardiovascular Society functional grade and treadmill exercise test results among angioplasty patients.32 Research on self-report measures has found that heavy-intensity physical activities are more accurately and reliably measured than light-intensity and moderate-intensity activities33,34 and may indeed be sufficient estimates of functional capacity.35

The purpose of this study was to examine the joint impact and interaction of smoking and self-reported exercise capacity on cardiovascular events, including heart failure (HF), myocardial infarction (MI), stroke, and cardiovascular-related death, over a median of 5.9 years within a sample of women with suspected myocardial ischemia.

Materials and Methods

Study design

Women undergoing angiography for suspected myocardial ischemia at one of four sites (University of Alabama at Birmingham; University of Florida, Gainesville; University of Pittsburgh, Pittsburgh, Pennsylvania; and Allegheny General Hospital, Pittsburgh) were enrolled from 1996 to 1999 in the Women's Ischemia Syndrome Evaluation (WISE) study. The complete design and methodology of the WISE study are described elsewhere.36

Each woman's demographic characteristics and cardiovascular risk profile, including her self-reported history of smoking, recent exercise capacity, and history of other CVD risk factors, were gathered in an extensive baseline evaluation. The women also underwent a physical examination that included height and weight measurements, which were used to calculate body mass index (BMI) using the standard formula (weight in kilograms/height in meters2). The WISE Angiographic Core Laboratory, which was blinded to all other subject data, analyzed coronary angiograms using a quantitative method described in detail elsewhere.37 The angiogram results were used to assign each participant a continuous coronary disease severity score based on a modified Gensini index.38 All participants provided written informed consent that was approved by the institutional review board (IRB) at their local WISE clinical site. Although 936 women were enrolled in WISE, this study examined the 789 for whom data were available on all variables included in the analyses.

Smoking and exercise measurement

Exercise capacity

Each woman completed the DASI questionnaire, a measure of self-assessed functional capacity, on which she indicated whether or not she could engage in each of 12 activities before her recent illness. Three of the 12 questions on the DASI address purposeful exercise activities, namely, running a short distance, participating in moderate sports, and participating in strenuous sports. Women who endorsed the capability of doing at least one of these 3 activities without difficulty were grouped together and labeled high exercise capacity (HEC); those who indicated that they were not able to do any of the 3 (or were able to with difficulty) formed the comparison group and were labeled low exercise capacity (LEC). Although these activities are not the only indicators of high or low exercise capacity, they uniquely capture purposeful exercise, are representative of more vigorous physical activities, and are thus more likely to be reported accurately and reliably than are other activities on the measure.3335

Smoking variables

Each woman was queried about her general smoking history (current, former, or never smoker). A dichotomous smoking variable was formed, with current and former smokers grouped together (smokers) and never smokers comprising the other category (nonsmokers). We initially assessed smokers in separate groups of current, former, and never. However, no statistical differences or trends were found between current and former smokers, so we combined them for final analyses. Past research has demonstrated the prognostic ability of similar simple smoking categories.39,40 Former and current smokers reported the number of years and the average number of cigarettes per day they had smoked. These numbers were multiplied together to form a pack-years variable. In addition, former smokers reported the number of months that had passed since they quit smoking.

Joint impact of smoking and exercise capacity

WISE participants were stratified into four groups based on smoking history and self-reported ability to engage in purposeful exercise: (1) LEC smokers (n = 297), (2) HEC smokers (n =118), (3) LEC nonsmokers (n = 265), and (4) HEC nonsmokers (n = 112).

Interaction between smoking and exercise capacity

An interaction term was created by calculating the cross product of smoking history and self-reported ability to engage in purposeful exercise in order to assess whether the two risk factors' relationships with clinical outcomes were influenced by or dependent on one another.

Clinical event tracking

Women were contacted via telephone or mail or both 6 weeks postbaseline and annually thereafter for a median of 5.9 years (interquartile range 3.6–6.9 years) to track their subsequent adverse cardiovascular events (HF, stroke, MI) and mortality, together referred to as clinical events. Self-reported cardiac events were confirmed by review of medical records. Death certificates were obtained to confirm deaths reported by significant others. For purposes of the current study, deaths were only included in the event total if they were determined to be probably (n = 21) or definitely (n = 41) due to cardiovascular-related reasons. Other deaths were classified as not related to cardiovascular reasons (n = 14), probably not due to cardiovascular reasons (n = 9), and of unknown origin (n = 10).

Statistics

Comparisons of demographic and cardiovascular risk factor characteristics among the smoking and exercise groups were completed using one-way analysis of variance (ANOVA). An a priori decision was made to conduct t tests of individual between-group differences, evaluated for significance using Dunnett's T3 corrections, as equal variances were not assumed, when overall group differences were detected. The same computations were made in order to examine differences among the four groups in the mean numbers of combined events per woman.

Unadjusted and covariate-adjusted hazard ratios (HR) and 95% confidence intervals (CI) were computed using Cox regression to examine differences in the time to first event in two different models: (1) the joint impact of smoking history and exercise capacity, using the four categorical groups, with HEC nonsmokers as the reference category, and (2) the interaction between smoking history and exercise capacity. Covariates in the adjusted survival analyses included the following: age, race (white vs. nonwhite), education (high school graduate equivalent or less vs. any postsecondary education), BMI, history of hypertension, history of dyslipidemia, history of diabetes, history of hormone replacement therapy (HRT) use, coronary artery disease (CAD) severity score, pack-years of smoking, and months since quitting smoking (coded as 0 for current and never smokers).

Population attributable risk (PAR) values and accompanying 95% CIs were computed using StatsDirect Statistical Software (www.statsdirect.com/) to further assess the independent and joint impacts of smoking and LEC on clinical event risk. All other analyses were completed using Statistical Package for the Social Sciences (SPSS), version 15 (Chicago, IL), and the significance criterion was set at 0.05 for declaring statistically significant differences.

Results

Baseline variables

Demographic variables and covariates, categorized by combined smoking and exercise capacity groups, are listed in Table 1. Notably, among the 65% of women who reported being former smokers, quit dates averaged nearly 13 years prior to study entry, and LEC smokers' pack-years were higher than those of HEC smokers. Furthermore, overall group differences were apparent on many of the covariates, including age, education, history of diabetes and dyslipidemia, and CAD severity score. Individual group differences calculated for these variables revealed the sources of the overall significant differences (Table 1). Although no systematic pattern of individual group differences emerged, all possible pairs of groups except for the two LEC and the two HEC groups differed on at least one variable.

Table 1.
Baseline Demographic Characteristics and Covariates for 789 WISE Women, Enrolled at One of Six Sites in 1997–1999 and Tracked for a Median of 5.9 Years

Smoking/exercise capacity and clinical events

Total numbers and means of events per woman over follow-up, broken down by smoking and exercise capacity groups, are presented in Table 2. All major adverse cardiovascular events and death were counted in this analysis whether women experienced the same event more than once or a combination of different events. Overall, 137 women experienced at least one event, including 45 who experienced more than one event. Women with LEC, regardless of smoking history, disproportionately comprised the multiple events cells, as only 4 women with HEC (all smokers) experienced more than one event. An unadjusted univariate ANOVA comparing the mean number of events per woman within each group resulted in an overall significant group difference (F = 6.86, p < 0.001). Planned between-group contrasts revealed that individual mean event differences were significant between HEC nonsmokers and each of the three other groups (all p < 0.005), as well as between HEC smokers and LEC smokers (p = 0.019).

Table 2.
Numbers of Clinical Events by Smoking and Exercise Capacity Group for 789 WISE Women, Enrolled at One of Six Sites in 1997–1999 and Tracked for Median of 5.9 Years

Results for unadjusted and adjusted Cox regression analyses examining the joint impact of smoking history and exercise capacity, with time to first event as the dependent variable and HEC nonsmokers as the reference group, are displayed in Table 3. HEC smokers and LEC nonsmokers' risks of experiencing an event were nearly identical in the unadjusted analysis. As additional adjustments were made for other covariates at each step of the analysis, results changed such that LEC smokers and HEC smokers' risks became increasingly similar. Moreover, unadjusted and adjusted analyses alike revealed that an event was significantly less likely to occur among HEC nonsmokers than among women in any other group. Approximately 83% (n = 652) of women remained event-free at the end of the follow-up period, ranging from 76% of LEC smokers to 97% of HEC nonsmokers.

Table 3.
Risk of Experiencing at Least One Clinical Event over Follow-Up for 789 WISE Women, Enrolled at One of Six Sites in 1997–1999 and Tracked for Median of 5.9 Years

Results for unadjusted and adjusted Cox regression analyses examining the interaction between smoking history and exercise capacity, with time to first event as the dependent variable, revealed a significant interaction (p = 0.023 for interaction term in adjusted model). Follow-up models examining the simple effects of smoking history and exercise capacity revealed the nature of this interaction. LEC posed an increased risk of event for women with a positive smoking history but not never smokers in both unadjusted and adjusted analyses. A positive history of smoking posed a significantly increased risk of events among LEC and HEC women in unadjusted analyses and models adjusted for all demographic and cardiovascular risk factors (listed in Materials and Methods). The final model further adjusted for pack-years of smoking and months quit. Smoking history still posed a significant risk of events among HEC women (HR = 12.73, 95% CI 2.8, 57.9) but was no longer significant among LEC women; however, pack-years of smoking was significant in this model (HR = 1.001, 95% CI 1.000, 1.001).

Population attributable risk (PAR)

Population attributable risk percent (PAR%) is the percentage of the incidence of a disease in the population (exposed and nonexposed) that is a result of exposure41 (e.g., smoking, LEC, or both). PAR% values and accompanying 95% CIs for smoking and LEC independently and in combination are listed in Table 4. Notably, the observed PAR% value was higher for LEC alone (46.9%) than for smoking alone (27.8%), suggesting that a greater global risk may be associated with LEC than with smoking. The highest PAR value was obtained for the LEC smokers (81.8%), indicating that LEC and smoking have additive effects and that nearly 82% of the risk in this cohort of women could potentially be reduced if all women had high self-reported exercise capacity and negative smoking histories.

Table 4.
Population Attributable Risk Percent (PAR%) for Smoking and Exercise Capacity for 789 WISE Women, Enrolled at One of Six Sites in 1997–1999 and Tracked for Median of 5.9 Years

Discussion

This study examined the joint impact and interaction of smoking history and self-reported exercise capacity on cardiovascular events (HF, stroke, MI) and all-cause mortality over a median of 5.9 years of follow-up within a sample of women with suspected myocardial ischemia at baseline. Although smoking and low exercise capacity are both well-established individual risk factors, existing literature does not address their potentially additive or interactive effects. Overall, results demonstrated that the risk of experiencing one or more events was lowest among HEC nonsmokers and highest among LEC smokers. HEC smokers' adjusted risk was nearly as high as that of LEC smokers, and LEC non-smokers experienced an intermediate risk. Moreover, a significant interaction between smoking and exercise capacity indicated that the additional risk posed by either factor depends on the other's presence.

The fact that current and former smokers' risks did not differ when smoking status was examined alone provides support for the finding in this study and others42,43 that the benefits of quitting smoking are not great enough to outweigh the risk of smoking at all. This finding should be further replicated in order to determine whether it is typical or a result of smaller subgroups, as larger studies such as INTERHEART11 have found risk differences between current and former smokers. Currently, smokers are often told that they can reduce or even eliminate their increased risk of negative health outcomes by quitting, but the results of this study imply that these benefits may be modest or delayed, at least for some women. Alternative explanations are possible, however. For example, the design of this study did not enable us to examine whether previous or ongoing health problems had led many of the former smokers' physicians to advise them to quit; if so, perhaps the former smokers were generally unhealthier than both the current and never smokers before this study began and were thus at greater risk of clinical outcomes for reasons other than the negative effects of their former smoking habit. Although we controlled for many cardiovascular risk factors, we could not control for all concurrent or previous health problems that may have affected clinical outcomes as well as smoking status and exercise capacity at baseline. For example, LEC in some participants may have been the result of long-term periods of inactivity because of preexisting health conditions ranging from physical limitations or overweight/obesity (sample's mean BMI = 29.5) to depression, many of which are CVD risk factors themselves. Moreover, a positive smoking history may have also negatively affected exercise capacity, as smoking is notorious for reducing lung function.

Although smoking appeared to be a stronger predictor of poor outcomes than LEC according to Cox regression results, the PAR% for LEC was higher than that for smoking. As PAR% values incorporate prevalence, this finding is likely indicative of the higher base rate of LEC compared with that of smoking in this sample. Interpreted together, these results suggest that although each risk factor independently predicts cardiovascular-related health outcomes, the joint effects offer the most useful approach for forecasting risk. However, the possibility that women's underlying CAD or health consequences from smoking had already incapacitated them from engaging in purposeful exercise cannot be dismissed. Because the CIs of the three groups with at least one risk factor overlapped, no definitive conclusions can be drawn about whether smoking history or low exercise capacity presents a greater overall risk.

This investigation makes important contributions to the extensive smoking and exercise capacity literatures. For example, it examines two risk factors, generally modifiable via health behaviors, both in combination and without other variables that are typically included in studies that investigate multiple risk factors. Many of these other factors were controlled for in the models presented in this paper, but they were not of primary interest. In addition, this study primarily focused on exercise capacity based on self-reported ability to engage in purposeful exercise rather than in general physical activity. Past research has demonstrated that this type of exercise may contribute to better health and longevity beyond that promoted by general activity.23,24 Our findings indicate that the administration of a subset of purposeful exercise items from the DASI can accurately capture exercise capacity information that predicts cardiovascular risk.

The present investigation builds on those of past WISE studies that have investigated the impact of exercise capacity or physical activity or both on clinical outcomes16,31,44 by including the smoking component and by selecting three high-impact questions from the DASI to determine if risk levels can be estimated in a simpler, more efficient way. The results from this study have practical implications from a public health perspective beyond those reported in past WISE studies: physicians can easily assess self-reported smoking history and high vs. low exercise capacity, defined by three questions that are more likely to be reported accurately and reliably than general activities typically listed on questionnaires.3335 Research demonstrates that physicians do not consistently assess smoking and exercise capacity among patients or advise them to quit smoking and increase their activity45 despite the fact that their advice on such issues can indeed influence patients' behaviors.46 Results from this study and others with similar results may lead to the practical tools that will be critical in the quick yet effective evaluation and documentation of physical capacity in an era with increased emphasis on pay for performance. Former and current smokers in this sample of symptomatic women did not differ in their risk of events. Therefore, we cannot conclude with certainty that similar women's cardiovascular prognosis would improve even if they heeded their physicians' advice to stop smoking or increase their exercise levels in an effort to increase their exercise capacity.

Study limitations

The demographic and clinical characteristics of the WISE sample, with a specific focus on mostly Caucasian women with suspected myocardial ischemia, differ from those of many other study populations, including studies comprising men, healthy women, or women with known CAD. Considering the vast literature on smoking and exercise capacity as individual risk factors for cardiovascular-related disease and mortality in a variety of other populations, however, the assumption that similar results of their joint impact would also be found in other populations is not improbable. In addition, although the reliability and validity of self-reported exercise capacity has been demonstrated in many studies, these results likely would have been more precise and compelling if they had been obtained using objectively measured exercise capacity. Likewise, self-reported exercise capacity on the DASI does not necessarily reflect long-term physical activity or exercise patterns, despite the established correlation between exercise capacity and regular physical activity/exercise. Also, we included HF in the clinical events total even though not all HF cases have coronary-related etiologies; nonetheless, HF is a cardiovascular-related outcome regardless of its etiology. Furthermore, the smoking and exercise capacity information used in this study was gathered at baseline only; many of these women may have altered their lifestyles or modified their risk factor profiles via medications within the median 5.9 years of follow-up in this study, potentially modifying their risk of events over time. Repeated assessments of risk factors over time would improve future research.

Conclusions

Among women with signs and symptoms of myocardial ischemia, the joint impact of, as well as the interaction between, self-reported exercise capacity and smoking history predicted long-term cardiovascular prognosis. Compared with HEC nonsmokers, all other groups of women experienced worse prognoses, even after adjusting for other risk factors. Moreover, a positive history of smoking posed a greater risk regardless of exercise capacity, whereas LEC posed a greater risk only among women with no history of smoking. The results of this study reinforce the harmful effects of smoking and LEC; in addition, they provide evidence that these primary risk factors' joint impact and interaction are also important in the evaluation of susceptible women's risk of adverse events and should be further studied in other populations.

Acknowledgments

This work was supported by contracts from the National Heart, Lung, and Blood Institute, N01-HV-68161, N01-HV-68162, N01-HV-68163, and N01-HV-68164, a GCRC grant M01-RR00425 from the National Center for Research Resources, and grants from the Gustavus and Louis Pfeiffer Research Foundation, The Women's Guild, Cedars-Sinai Medical Center, and the Ladies Hospital Aid Society of Western Pennsylvania, and QMED, Inc.

Disclosure Statement

No competing financial interests exist.

References

1. Unwin N. Alberti K. Chronic non-communicable diseases. Ann Trop Med Parasitol. 2006;100:455–464. [PubMed]
2. Mokdad A. Marks J. Stroup D, et al. Actual causes of death in the United States, 2000. JAMA. 2004;291:1238–1245. [PubMed]
3. Centers for Disease Control and Prevention, Department of Health and Human Services, National Center for Chronic Disease Prevention and Health Promotion. Division for heart disease and stroke prevention: Addressing the nation's leading killers, at a glance. www.cdc.gov/nccdphp/publications/AAG/dhdsp.htm. www.cdc.gov/nccdphp/publications/AAG/dhdsp.htm
4. U.S. Department of Health and Human Services. Healthy people 2010: Understanding and improving health. www.health.gov/healthypeople/ www.health.gov/healthypeople/ Document/HTML/volume1.
5. Fleg JL. Piña IL. Balady GJ, et al. Assessment of functional capacity in clinical and research applications. An advisory from the Committee on Exercise, Rehabilitation, and Prevention, Council on Clinical Cardiology, American Heart Association. Circulation. 2000;102:1591–1597. [PubMed]
6. Myers J. Kaykha A. George S, et al. Fitness versus physical activity patterns in predicting mortality in men. Am J Med. 2004;117:912–918. [PubMed]
7. Morris CK. Ueshima K. Kawaguchi T, et al. The prognostic value of exercise capacity: A review of the literature. Am Heart J. 1991;122:1423–1431. [PubMed]
8. White WB. Smoking-related morbidity and mortality in the cardiovascular setting. Prev Cardiol. 2007;10(Suppl 1):1–4. [PubMed]
9. Erikssen G. Physical fitness and changes in mortality: The survival of the fittest. Sports Med. 2001;31:571–576. [PubMed]
10. Blair SN. Kampert JB. Kohl HW III, et al. Influences of cardiorespiratory fitness and other precursors on cardiovascular disease and all-cause mortality in men and women. JAMA. 1996;276:205–210. [PubMed]
11. Yusuf S. Hawken S. Ôunpuu S, et al. on behalf of the INTERHEART Study Investigators. Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): Case-control study. Lancet. 2004;364:937–952. [PubMed]
12. Fine LJ. Philogene S. Gramling R. Coups EJ. Sinha S. Prevalence of multiple chronic disease risk factors: 2001 National Health Interview survey. Am J Prev Med. 2004;27(2S):18–24. [PubMed]
13. CDC. Cigarette smoking among adults—United States, 2004. MMWR. 2005;54:1121–1124. [PubMed]
14. Barnes PM. Schoenborn CA. Advance data from vital and health statistics. Hyattsville, MD: National Center for Health Statistics; 2003. Physical activity among adults: United States, 2000. No. 333.
15. Carnethon MR. Gulati M. Greenland P. Prevalence and cardiovascular disease correlates of low cardiorespiratory fitness in adolescents and adults. JAMA. 2005;294:2981–2988. [PubMed]
16. Wessel TR. Arant CB. Olson MB, et al. Relationship of physical fitness vs. body mass index with coronary artery disease and cardiovascular events in women. JAMA. 2004;292:1179–1187. [PubMed]
17. Gulati M. Black HR. Shaw LJ, et al. The prognostic value of a nomogram for exercise capacity in women. N Engl J Med. 2005;353:468–475. [PubMed]
18. U.S. Department of Health and Human Services. Physical activity and health: A report of the Surgeon General. Atlanta, GA: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention National Center for Chronic Disease Prevention and Health Promotion; 1996.
19. Macera CA. Powell KE. Population attributable risk: Implications of physical activity dose. Med Sci Sports Exerc. 2001;33:S635–639. [PubMed]
20. Warburton DER. Nicol CW. Bredin SSD. Health benefits of physical activity: The evidence. Can Med Assoc J. 2006;174:801–809. [PMC free article] [PubMed]
21. Brach JS. Simonsick EM. Kritchevsky S, et al. The association between physical function and lifestyle activity and exercise in the health, aging and body composition study. J Am Geriatr Soc. 2004;52:502–509. [PubMed]
22. Manson JE. Greenland P. LaCroix Z, et al. Walking compared with vigorous exercise for the prevention of cardiovascular events in women. N Engl J Med. 2002;347:716–725. [PubMed]
23. Lee IM. Paffenbarger RS. Associations of light, moderate, and vigorous intensity physical activity with longevity. The Harvard Alumni Health Study. Am J Epidemiol. 2000;151:293–299. [PubMed]
24. O'Donovan G. Owen A. Bird SR, et al. Changes in cardiorespiratory fitness and coronary heart disease risk factors following 24 weeks of moderate- or high-intensity exercise of equal energy cost. J Appl Physiol. 2005;98:1619–1625. [PubMed]
25. Kohl HW., 3rd Physical activity and cardiovascular disease: Evidence for a dose response. Med Sci Sports Exerc. 2001;33(Suppl 6):S472–483. [PubMed]
26. Mosca L. Grundy SM. Judelson D, et al. AHA/ACC scientific statement: Consensus panel statement: Guide to preventive cardiology for women. Circulation. 1999;99:2480–2484. [PubMed]
27. Asikainen TM. Kukkonen-Harjula K. Miilunpalo S. Exercise for health for early postmenopausal women: A systematic review of randomized controlled trials. Sports Med. 2004;34:753–758. [PubMed]
28. Ainsworth BE. Sternfeld B. Richardson MT, et al. Evaluation of the Kaiser physical activity survey in women. Med Sci Sports Exerc. 2000;32:1327–1338. [PubMed]
29. McAuley P. Myers J. Abella J, et al. Evaluation of a specific activity questionnaire to predict mortality in men referred for exercise testing. Am Heart J. 2006;151:891–897. [PubMed]
30. Hlatky MA. Boineau RE. Higginbotham MB, et al. A brief self-administered questionnaire to determine functional capacity (the Duke Activity Status Index) Am J Cardiol. 1989;64:651–654. [PubMed]
31. Bairey Merz CN. Olson M. McGorray S, et al. Physical activity and functional capacity measurement in women: A report from the NHLBI-sponsored WISE Study. J Womens Health. 2000;9:769–777. [PubMed]
32. Alonso J. Permanyer-Miraldaf J. Cascantf P, et al. Measuring functional status of chronic coronary patients. Reliability, validity and responsiveness to clinical change of the reduced version of the Duke Activity Status Index (DASI) Eur Heart J. 1997;18:414–419. [PubMed]
33. Ainsworth BE. Jacobs DR Jr. Leon AS. Validity and reliability of self-reported physical activity status: The Lipid Research Clinics questionnaire. Med Sci Sports Exerc. 1993;25:92–98. [PubMed]
34. Jacobs DR Jr. Ainsworth BE. Hartman TJ, et al. A simultaneous evaluation of 10 commonly used physical activity questionnaires. Med Sci Sports Exerc. 1993;25:81–91. [PubMed]
35. Deyo RA. Carter WB. Strategies for improving and expanding the application of health status measures in clinical settings. A researcher-developer viewpoint. Med Care. 1992;30(Suppl 5):MS176–186. [PubMed]
36. Bairey Merz CN. Kelsey SF. Pepine CJ, et al. The Women's Ischemia Syndrome Evaluation (WISE) study: Protocol design, methodology and feasibility report. J Am Coll Cardiol. 1999;33:1453–1461. [PubMed]
37. Sharaf BL. Pepine CJ. Kerensky RA, et al. Detailed angiographic analysis of women with suspected ischemic chest pain (pilot phase data from the NHLBI-sponsored Women's Ischemia Syndrome Evaluation [WISE] study angiographic core laboratory) Am J Cardiol. 2001;87:937–941. [PubMed]
38. Sharaf BL. Williams DO. Miele NJ, et al. A detailed angiographic analysis of patients with ambulatory electrocardiographic ischemia: Results from the Asymptomatic Cardiac Ischemia Pilot (ACIP) study angiographic core laboratory. J Am Coll Cardiol. 1997;29:78–84. [PubMed]
39. Giovino GA. Schooley MW. Zhu BP, et al. Surveillance for selected tobacco-use behaviors—United States, 1900–1994. MMWR CDC Surveill Summ. 1994;43:1–43. [PubMed]
40. Hughes E. McCracken M. Roberts H, et al. Surveillance for certain health behaviors among states and selected local areas—Behavioral Risk Factor Surveillance System, United States, 2004. MMWR Surveill Summ. 2006;55:1–124. [PubMed]
41. Basu S. Landis JR. Model-based estimation of population attributable risk under cross-sectional sampling. Am J Epidemiol. 1995;142:1338–1343. [PubMed]
42. Kondo T. Hayashi M. Takeshita K, et al. Smoking cessation rapidly increases circulating progenitor cells in peripheral blood in chronic smokers. Arterioscler Thromb Vasc Biol. 2004;24:1442–1447. [PubMed]
43. Celermajer DS. Sorensen KE. Georgakopoulos D, et al. Cigarette smoking is associated with dose-related and potentially reversible impairment of endothelium-dependent dilation in healthy young adults. Circulation. 1993;88:2149–2155. [PubMed]
44. Shaw LJ. Olson MB. Kip K, et al. The value of estimated functional capacity in estimating outcome: Results from the NHBLI-sponsored Women's Ischemia Syndrome Evaluation (WISE) study. J Am Coll Cardiol. 2006;47(Suppl 3):S36–43. [PubMed]
45. Stange KC. Flocke SA. Goodwin MA, et al. Direct observation of rates of preventive service delivery in community family practice. Prev Med. 2000;31:167–176. [PubMed]
46. Gorin SS. Heck JE. Meta-analysis of the efficacy of tobacco counseling by health care providers. Cancer Epidemiol Biomarkers Prev. 2004;13:2012–2022. [PubMed]

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